REMI: Resource for Materials Informatics

The REsource for Materials Informatics (REMI) will host a diverse collection of scripting notebooks (Jupyter, Matlab LiveScripts, etc.) for collecting, pre-processing, analyzing, and visualizing materials data. Notebooks are curated using tags aligned to Materials Science and Data Science topics. REMI emerged from the realization that both experts and novices wanted examples of using machine learning for science. Meanwhile, lots of experts are developing digital notebooks (e.g. Jupyter) to demonstrate step-by-step data collection, pre-processing, analysis and visualization. However, before REMI there were no indexed repositories of notebooks with such examples and no community to maintain, build or learn from these resources. REMI aims to fill this gap, enabling scientists to more easily pick up machine learning while also helping to build a community for sharing knowledge, discussing, debating, establish collaborations, and benchmarking methods.

Data and Resources

Field Value
accessLevel public
accrualPeriodicity R/P1W
bureauCode {006:55}
catalog_@context https://project-open-data.cio.gov/v1.1/schema/data.json
catalog_conformsTo https://project-open-data.cio.gov/v1.1/schema
catalog_describedBy https://project-open-data.cio.gov/v1.1/schema/catalog.json
identifier ark:/88434/mds2-2306
issued 2020-10-23
landingPage https://pages.nist.gov/remi/
language {en}
license https://www.nist.gov/open/license
modified 2020-09-25 00:00:00
programCode {006:045}
publisher National Institute of Standards and Technology
resource-type Dataset
source_datajson_identifier true
source_hash 73c1d1b3708dd2b2573524e529bbf4d40bdd7818
source_schema_version 1.1
theme {"Information Technology:Computational science","Information Technology:Data and informatics","Mathematics and Statistics:Experiment design","Mathematics and Statistics:Image and signal processing","Mathematics and Statistics:Statistical analysis","Mathematics and Statistics:Uncertainty quantification","Mathematics and Statistics:Numerical methods and software","Materials:Materials characterization","Materials:Modeling and computational material science"}
Groups
  • AmeriGEOSS
  • National Provider
  • North America
Tags
  • amerigeo
  • amerigeoss
  • ckan
  • data-analysis
  • data-processing
  • geo
  • geoss
  • machine-learning
  • materials-genome-initiative
  • materials-science
  • national
  • north-america
  • united-states
isopen False
license_id other-license-specified
license_title other-license-specified
maintainer Aaron Gilad Kusne
maintainer_email aaron.kusne@nist.gov
metadata_created 2025-11-22T20:25:22.673051
metadata_modified 2025-11-22T20:25:22.673055
notes The REsource for Materials Informatics (REMI) will host a diverse collection of scripting notebooks (Jupyter, Matlab LiveScripts, etc.) for collecting, pre-processing, analyzing, and visualizing materials data. Notebooks are curated using tags aligned to Materials Science and Data Science topics. REMI emerged from the realization that both experts and novices wanted examples of using machine learning for science. Meanwhile, lots of experts are developing digital notebooks (e.g. Jupyter) to demonstrate step-by-step data collection, pre-processing, analysis and visualization. However, before REMI there were no indexed repositories of notebooks with such examples and no community to maintain, build or learn from these resources. REMI aims to fill this gap, enabling scientists to more easily pick up machine learning while also helping to build a community for sharing knowledge, discussing, debating, establish collaborations, and benchmarking methods.
num_resources 1
num_tags 13
title REMI: Resource for Materials Informatics